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.. _stat_learn_tut_index:
==========================================================================
A tutorial on statistical-learning for scientific data processing
==========================================================================
.. topic:: Statistical learning
`Machine learning <http://en.wikipedia.org/wiki/Machine_learning>`_ is
a technique with a growing importance, as the
size of the datasets experimental sciences are facing is rapidly
growing. Problems it tackles range from building a prediction function
linking different observations, to classifying observations, or
learning the structure in an unlabeled dataset.
This tutorial will explore `statistical learning`, that is the use of
machine learning techniques with the goal of `statistical inference
<http://en.wikipedia.org/wiki/Statistical_inference>`_:
drawing conclusions on the data at hand.
``sklearn`` is a Python module integrating classic machine
learning algorithms in the tightly-knit world of scientific Python
packages (`numpy <http://www.scipy.org>`_, `scipy
<http://www.scipy.org>`_, `matplotlib
<http://matplotlib.sourceforge.net/>`_).
.. include:: ../../includes/big_toc_css.rst
.. warning::
In scikit-learn release 0.9, the import path has changed from
`scikits.learn` to `sklearn`. To import with cross-version
compatibility, use::
try:
from sklearn import something
except ImportError:
from scikits.learn import something
.. toctree::
:maxdepth: 2
settings
supervised_learning
model_selection
unsupervised_learning
putting_together
finding_help
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